Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

by Yang Xiang

• ISBN13: 9780521153904
• ISBN10: 0521153905
• Format: Paperback
• Copyright: 2010-06-24
• Publisher: Cambridge University Press

Summary

This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

Table of Contents

Preface

Introduction

Bayesian networks

Belief updating and cluster graphs

Junction tree representation

Belief updating with junction trees

Multiply sectioned Bayesian networks

Linked junction forests

Distributed multi-agent inference

Model construction and verification

Looking into the future

Bibliography

Index

Table of Contents provided by Publisher. All Rights Reserved.